## Warning: package 'kableExtra' was built under R version 3.5.2
Eatomics is an R-Shiny based web application that enables interactive exploration of quantitative proteomics data generated by MaxQuant software. Eatomics enables fast exploration of differential expression and pathway analysis to researchers with limited bioinformatics knowledge. The application aids in quality control of the quantitative proteomics data, visualization, differential expression and pathway analysis. Highlights of the application are an extensive experimental setup module, the data and report generation feature and the multiple ways to interact and customize the analysis.
Eatomics requires two file inputs:
Demo_proteinGroups.txt: The proteinGroups.txt (i.e. a tab-separated files) as generated by the quantitative analysis software of raw mass spectrometry data - MaxQuant. The file should contain at least the columns Protein IDs, Majority protein IDs, Gene names, LFQ/iBAQ measurement columns, Reverse, Potential contaminant, Only identified by site. The latter three may be empty.
Demo_clinicaldata.txt: The sample description file - a tab separated text file as can be produced with any Office program by saving the spread sheet as .txt. The file needs to contain a column named “PatientID”, which contains IDs that match the sample ID’s from the proteinGroups header (without the “LFQ intensity” or “iBAQ” prefixes) and one or more named columns with “parameters”, i.e. textual/factual/logical or continuous/integer values. Column names have to be unique.
The name of the samples shall be identical in both input files. Also, represent character variables as ‘factor’ and integers as ‘numeric’. Please note that ordinal values are treated as numeric. text
Access to demo data is possible directly via the upload button if ou are testing on our public server. For your local installation you may directly use your own data or the demo files in Eatomics/Data from the github repository.Eatomics functionality is structured into four tab panels:
All tabs consist of a side panel to configure the analysis and a main panel for interactive analysis visualization.
The first tab provides an overview on the data quality and enables filtering and preparation of data for differential expression and enrichment analysis ().
Within the side panel the user can load data and configure quality control options.
To begin the analysis the user has to upload the MaxQuant file (e.g.proteinGroups.txt), as specified above. After full upload of the file, rows that were only found in the reverse database, belonging to potential contaminants or that have only been identified by site are filtered automatically.
The user selects either LFQ (Label-free quantification) or iBAQ (Intensity Based Absolute Quantification) as intensity metric to be considered for succeeding differential expression analysis. If available, we suggest to use LFQ intensities as Eatomics was optimized for these. Internally, the intensity widget uses the selectProteinData function.
The exclude column widget allows the user to exclude samples, especially if any outliers are found while conducting initial quality analysis such as PCA. Selecting a sample here, results in the removal of that sample from the consecutive steps analysis steps.
To avoid proteins with many missing values across the samples, the user selects the minimum number of samples for which a protein must have been detected in. Internally the filter widget uses the filterProteins function.
Missing value imputation can be performed using knn (k-nearest-neighbour), MinDet or QRLIC from the imputeLCMD package or a custom implementation of Perseus’ sampling from Gaussian distribution (implemented by Matthias Ziehm).
Select and load the clinical data input file (e.g clinicaldata.txt), as specified above.
Configuration panel to load input data and to prepare the data set for analysis.
In the main panel (right) interactive visualizations are shown.
A common method of dimensionality reduction is principal component analysis (PCA). Inherently, PCA calculates axes of most variation (principal components) within the expression data. A common assumption is that a plot along the axes of most variation will segregate all samples/patients into groups under investigation. The user can choose which principle components to visualize in the PCA and can choose to color the samples based on the uploaded sample/clinical characteristics.
Visualization of protein abundance in a PCA.
Protein numbers describes the count of distinct proteins or isoforms per sample. The plot is generated by the plot_numbers() function from the DEP package which was adjusted to work without experimental design information.
Sample wise distribution overview of protein abundance data.
Protein numbers describes the count of distinct protein groups per sample.
Sample-wise coverage of protein abundance data.
The sample-to-sample heatmap describes the biological and/or technical variability of the samples. The user can choose to use Euclidean distance or Pearson correlation as a (dis-)similarity metric. Formed clusters should resemble the sample groups under investigation.
Sample to sample heatmap.
Protein intensities are cumulated across all samples and plotted according to their relative abundance. Colouring marks the respective quantile of the proteins. Highly abundant proteins, i.e., proteins ranked in the first quartile are colored in red and labels are specified. The top 20 ranked proteins and their cumulated intensity are given in the table to the right.